Network defenses based on traditional tools, techniques, and procedures (TTP) fail to account for the attacker’s inherent advantage present due to the static nature of network services and configurations. To take away this asymmetric advantage, Moving Target Defense (MTD) continuously shifts the configuration of the underlying system, in turn reducing the success rate of cyberattacks. In this survey, we analyze the recent advancements made in the development of MTDs and highlight (1) how these defenses can be defined using common terminology, (2) can be made more effective with the use of artificial intelligence techniques for decision making, (3) be implemented in practice and (4) evaluated. We first define an MTD using a simple and yet general notation that captures the key aspects of such defenses. We then categorize these defenses into different sub-classes depending on what they move, when they move and how they move. In trying to answer the latter question, we showcase the use of domain knowledge and game-theoretic modeling can help the defender come up with effective and efficient movement strategies. Second, to understand the practicality of these defense methods, we discuss how various MTDs have been implemented and find that networking technologies such as Software Defined Networking and Network Function Virtualization act as key enablers for implementing these dynamic defenses. We then briefly highlight MTD test-beds and case-studies to aid readers who want to examine or deploy existing MTD techniques. Third, our survey categorizes proposed MTDs based on the qualitative and quantitative metrics they utilize to evaluate their effectiveness in terms of security and performance. We use well-defined metrics such as risk analysis and performance costs for qualitative evaluation and metrics based on Confidentiality, Integrity, Availability (CIA), attack representation, QoS impact, and targeted threat models for quantitative evaluation. Finally, we show that our categorization of MTDs is effective in identifying novel research areas and highlight directions for future research.

Virtual reality (VR) is considered to be one of the main use cases of the fifth-generation cellular system (5G). In addition, it has been categorized as one of the ultra-low latency applications in which VR applications require an end-to-end latency of 5 ms. However, the limited battery capacity and computing resources of mobile devices restrict the execution of VR applications on these devices. As a result, mobile edge-cloud computing is considered as a new paradigm to mitigate resource limitations of these devices through computation offloading process with low latency. To this end, this paper introduces an efficient multi-player with multi-task computation offloading model with guaranteed performance in network latency and energy consumption for VR applications based on mobile edge-cloud computing. In addition, this model has been formulated as an integer optimization problem whose objective is to minimize the sum cost of the entire system in terms of network latency and energy consumption. Afterwards, a low-complexity algorithm has been designed which provides comprehensive processes for deriving the optimal computation offloading decision in an efficient manner. Furthermore, we provide a prototype and real implementation for the proposed system using OpenAirInterface software. Finally, simulations have been conducted to validate our proposed model and prove that the network latency and energy consumption can be reduced by up to 26.2%, 27.2% and 10.9%, 12.2% in comparison with edge and cloud execution, respectively.

In adaptivity, the interface of the device automatically adjusts and assists the user. The adaptive user interfaces can adapt their activities by monitoring user status, the state of the system, and the current situation according to the adaptation strategy. Usually, the intensity of adaptation is measured in effectiveness, efficiency, and satisfaction to analyze the smartphone’s adaptive features. The adaptive features of light-emitting diode (LED) notifications, voice commands, face recognition, screen rotation, kid mode, drive mode, night mode, Swift Keyboard, s-health, gesture recognition, and fingerprint are selected for both iOS and Android platforms. Task completion within a specific time frame is used to measure effectiveness and efficiency, while satisfaction is calculated using the after-scenario questionnaire (ASQ). A total of 550 users are involved in the experimentation. The usability evaluation is measured for smartphone features. The effectiveness of adaptive features contains higher adaptivity in face recognition (87%) and voice command (85%). Furthermore, the satisfaction level is greater for adaptive features than non-adaptive features. This study indicates that adaptive features can only be used after a thorough examination of the user’s context. Furthermore, the usability evaluation shows that there is a dire need for adaptive smartphone features to provide ease and satisfaction to the user.

Cyber defense systems face increasingly sophisticated threats that rapidly evolve and exploit vulnerabilities in complex environments. Traditional approaches which often rely on centralized monitoring and static rule-based detection, struggle to adapt to new, crafted, and novel attack patterns. This paper presents the Adaptive Zero-Shot Hierarchical Multi-Agent Reinforcement Learning (AZH-MARL) framework, a novel approach that integrates hierarchical reinforcement learning, zero-shot learning capabilities, and federated knowledge sharing to build resilient cyber defense systems. The hierarchical structure decomposes complex defense tasks into specialized sub-tasks managed by agents, reducing the learning problem’s complexity and enabling more efficient coordination. The zero-shot learning component allows the framework to recognize and response to previously unseen attack patterns through semantic mapping. Furthermore, the federated learning learning component facilitates for knowledge sharing across network domains while preserving data privacy, enabling collaborative defense without exposing sensitive information. The detailed evaluation demonstrates that our approach significantly outperforms existing methods across a range of scenarios. It achieves a high detection rate of 94.2% for known attacks and 82.7% for zero-day exploits, while maintaining a low false positive rate of 3.8%. This robust performance extends to the most sophisticated threats, achieving an 87.3% containment rate against Advanced Persistent Threats (APTs). The framework’s zero-shot capability is underpinned by a semantic mapping accuracy of 89.3%, which enables rapid adaptation to novel threats. Consequently, the mean response time is reduced by 35% for known attacks and 42% for zero-day exploits compared to the best-performing baseline. Finally, the federated learning architecture proves highly efficient, reducing communication overhead by 45% while preserving privacy. These results collectively demonstrate our framework’s potential to set a new standard for resilient and adaptive cyber defense in complex, distributed environments.

The static nature of cyber defense systems gives attackers a sufficient amount of time to explore and further exploit the vulnerabilities of information technology systems. In this paper, we investigate a problem where multiagent systems sensing and acting in an environment contribute to adaptive cyber defense. We present a learning strategy that enables multiple agents to learn optimal policies using multiagent reinforcement learning (MARL). Our proposed approach is inspired by the multiarmed bandits (MAB) learning technique for multiple agents to cooperate in decision making or to work independently. We study a MAB approach in which defenders visit a system multiple times in an alternating fashion to maximize their rewards and protect their system. We find that this game can be modeled from an individual player’s perspective as a restless MAB problem. We discover further results when the MAB takes the form of a pure birth process, such as a myopic optimal policy, as well as providing environments that offer the necessary incentives required for cooperation in multiplayer projects.